Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit
Year: 2024
Type: article
Study design: Observational Study
Abstract: ABSTRACT Background Heart failure (HF) is a common condition that imposes a significant burden on healthcare systems. We aimed to identify subgroups of patients with heart failure admitted to the ICU using routinely measured laboratory biomarkers. Methods A large dataset (N = 1176) of patients with heart failure admitted to the ICU at the Beth Israel Deaconess Medical Center in Boston, USA, between June 1, 2001, and October 31, 2012, was analyzed. We clustered patients to identify laboratory phenotypes. Cluster profiling was then performed to characterize each cluster, using a binary logistic model. Results Two distinct clusters of patients were identified (N = 679 and 497). There was a significant difference in the mortality rate between Clusters 1 and 2 (50 [7.4%] vs. 109 [21.9%], respectively, p < 0.001). Patients in the Cluster 2 were significantly older (mean [SD] age = 72.35 [14.40] and 76.37 [11.61] years, p < 0.001) with a higher percentage of chronic kidney disease (167 [24.6%] vs. 262 [52.7%], respectively, p < 0.001). The logistic model was significant (Log‐likelihood ratio p < 0.001, pseudo R2 = 0.746) with an area under the curve of 0.905. The odds ratio for leucocyte count, mean corpuscular volume (MCV), red blood cell (RBC) distribution width, hematocrit (HcT), lactic acid, blood urea nitrogen (BUN), serum potassium, magnesium, and sodium were significant (all p < 0.05). Conclusion Laboratory data revealed two phenotypes of ICU‐admitted patients with heart failure. The two phenotypes are of prognostic importance in terms of mortality rate. They can be differentiated using blood cell count, kidney function status, and serum electrolyte concentrations.
DOI: 10.1002/jcla.25109
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,970
OpenAlex ID: W4403145857
